Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies
arXiv:2512.19673v3 Announce Type: replace Abstract: Existing reinforcement learning (RL) approaches treat large language models (LLMs) as a unified policy, overlooking their internal mechanisms. In this paper, we decompose the LLM-based policy into Internal Layer Policies and Internal Modular Policies via the Transformer's residual stream. Our entropy analysis of internal policy reveals distinct patterns: (1) universally, internal policies evolve from high-entropy exploration in early layers to deterministic refinement in the top layers; and (2) Qwen exhibits an explicit progressive reasoning
This research leverages an increasingly sophisticated understanding of LLM architectures and capabilities to deconstruct their internal workings, enabled by advanced interpretability techniques.
A strategic reader should care because decomposing LLM policies into internal layers could unlock more controllable, efficient, and interpretable AI agents, moving beyond monolithic 'black box' approaches.
Understanding LLMs as composed of multiple 'internal policies' rather than a single unified policy changes how we might design, optimize, and debug these powerful systems, offering new pathways for control.
- · AI researchers
- · LLM developers
- · AI interpretability tools
- · Monolithic black-box LLM approaches
- · Companies without LLM fine-grained control
The ability to inspect and dissect internal LLM decision-making processes improves their reliability and safety.
This deeper understanding could lead to more modular and specialized LLMs, potentially reducing compute requirements for specific tasks.
It might enable the ethical alignment of AI systems by directly modifying undesirable internal policies, rather than relying on external constraints.
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Read at arXiv cs.LG